Reliable execution of a robust soft computing workplace found on multiple neuro-fuzzy inference systems coupled with multiple nonlinear equations for exhaustive perception of tractor-implement performance in plowing process

Abstract

Tendency towards computer simulations linked to agricultural machinery has enormously increased in recent years. In this regard, the principal contribution of current research was to develop soft computing simulation workplaces for performance prognostication of tractor-implement system in plowing process. Two neuro-fuzzy strategies based on multiple adaptive neuro-fuzzy inference systems (MANFIS) scenario and the MANFIS coupled with multiple nonlinear equations (MNE) scenario were executed in the workplace. Additionally, neural strategy based on artificial neural network (ANN) scenario was also fulfilled in the workplace. Operational variables of plowing depth (10–30 cm), forward speed (2–6 km/h), and tillage implement type (moldboard, disk, and chisel plow) were considered as the workplace inputs and ten performance parameters were taken as the workplace outputs. According to the obtained prognostication accuracy, simulation time, and user-friendly configuration of three scenarios (ANN, MANFIS, and MANFIS+MNE), the MANFIS+MNE was recognized as the prominent simulation scenario. According to the MANFIS+MNE workplace results, for each tillage implement, the compound effect of plowing depth and forward speed on some performance parameters (required draft force of implement, tractor rear wheel slip, fuel consumption per working hour, specific volumetric fuel consumption, tractor drawbar power, energy requirement for tillage implement, overall energy efficiency, and tractor tractive efficiency) was nonlinearly synergetic. However, it was nonlinearly antagonism in case of specific draft force and fuel consumption per tilled area. The MANFIS+MNE workplace simulation results provide opportunity for technical farmer associations involved in the decision-making of agricultural machinery management in order to gain exhaustive fundamental insights into the compound effect of plowing depth and forward speed on performance of tractor-implement systems in plowing process.